基于因子列的阴影面
Shade facets base on a factor column
这是我的数据输入样本...
SKOV<-as.data.frame(structure(list(sample_id = c(147, 167, 18, 131, 17, 222, 270,
19, 173, 56, 265, 151, 250, 273, 17, 61, 324, 279, 161, 144,
312, 97, 88, 167, 112, 146, 30, 17, 52, 99, 188, 135, 184, 277,
93, 305, 37, 264, 253, 74, 299, 176, 93, 24, 162, 7, 209, 259,
85, 231), cluster = structure(c(3L, 3L, 1L, 3L, 3L, 2L, 1L, 1L,
3L, 3L, 1L, 3L, 2L, 1L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 3L, 3L,
1L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 1L, 2L, 3L,
2L, 3L, 3L, 1L, 2L, 1L, 1L, 2L, 3L, 3L), .Label = c("1", "2",
"3"), class = "factor"), PC1 = c(3.77541554972203, 1.45135092275582,
-2.08151488022046, 1.67392278661776, -0.868667705111095, 0.381346152049824,
-4.51990813842073, -4.33106175029972, 4.09484115180022, 1.46878082673118,
-3.91009236070008, 2.29855909444833, 1.28996975316864, -4.33482864589876,
-0.868667705111095, 1.71386877911134, 0.542339094466148, -5.42446660572953,
2.07724803679489, 3.1482373274481, 0.319519012725789, 1.35932545954681,
0.515976962035749, 1.45135092275582, -2.2956688398855, 3.12130887968216,
0.957815542171644, -0.868667705111095, 2.44038404221629, -3.73532585246298,
3.60298231127924, 1.76697731520925, 0.107335593841895, -4.24926391802412,
2.37822851276455, 0.863188301906724, 0.633358640568618, -4.49426173195687,
0.657329751847109, 1.07849849754947, 0.949226371513706, 2.20251690907612,
2.37822851276455, -5.27188001219925, 0.883552693512362, -3.65471472570778,
-4.17396534181865, 0.740413932072948, 2.35215180710275, 2.95723653631208
), PC2 = c(2.9821460311606, 1.84547428893804, -0.110154674255345,
0.94494220934088, 1.77529975625982, -1.75627062956882, 0.455969541574471,
1.01232365128454, 5.22885669105582, 0.614756183817838, 0.53924434139561,
0.824937608066585, -2.58029550589492, 0.599258675742853, 1.77529975625982,
-1.51359310918018, -2.94834599434092, 1.06948580574065, -1.53223407015238,
2.15612285827502, -3.11118425549662, 1.35847952064022, 1.52107198979851,
1.84547428893804, 0.600591536702616, 0.873482585063045, -2.66401802898751,
1.77529975625982, 1.72968551305468, 1.57607478071461, 4.6737233499792,
0.407336067372946, 2.67228082439395, 0.116730668123481, 3.76755528568674,
-3.38651333356804, -1.44641388322638, 0.154407055925795, -2.43105409429423,
0.644629065490083, -1.74893772151854, 2.74702223077597, 3.76755528568674,
1.36129565011855, -0.771286443151456, 1.26640510339257, -0.24592726728564,
-2.12450533889367, 1.59309393261233, 0.536480763301413), PC3 = c(-1.54368524007348,
2.43402556007619, -0.0450967184646642, 0.0410426039829931, -0.848801725782723,
0.38804666076165, -1.53725160414506, 0.00144436967650244, -0.219129975234483,
-0.549564648310951, 0.404446222294109, -0.2024943392322, -0.151863586122569,
0.595219147562226, -0.848801725782723, -0.546704676736899, 0.603107536227402,
-3.84773521184464, -1.12569943452765, -0.784183732377998, 0.954584210395496,
1.03620198922004, 2.88552053445592, 2.43402556007619, -6.37932899691359,
-0.440575910698521, 0.577066781163853, -0.848801725782723, 0.0762964729013986,
-0.197383547463127, -0.122216505063875, -0.152129782123152, 5.9139111613348,
0.419059172110045, 0.538964366314296, 0.288559910935336, 0.573620970938223,
-1.43282961184911, 0.273788799449918, 1.17945429175878, -0.939838485321215,
0.769679221867679, 0.538964366314296, 0.154922166837421, 0.640999296534156,
-0.213535320971047, 0.487374487637418, 0.530768949185841, 0.682768810474348,
-2.65577976399495), PC4 = c(0.513667495685775, -1.61889899346387,
-0.00199956789317688, 0.0852133725033396, 0.200110415721805,
-0.54476643944572, -0.665890345796119, 0.692033116930379, 0.68095691640909,
0.0715306855842227, 1.56182228831717, 0.836306992758303, -0.213466309266933,
1.25649126097911, 0.200110415721805, 0.329995819652711, -0.673049170161758,
-7.16504510762261, -0.00823529898926304, 0.636191153685101, -0.112239279821607,
-0.668345916270364, -2.22187934484341, -1.61889899346387, -5.79832117606908,
0.679388729196689, -0.565063309411897, 0.200110415721805, 0.277981223741711,
0.240500293392891, 0.424228959808187, 0.188958927083737, -4.88485900638901,
1.39045390873317, 0.044539479926183, -0.39937436237614, -0.127318966653892,
-3.92777799888502, 0.106028414733035, -1.35733137106087, 0.604053687622463,
-0.283098175950597, 0.044539479926183, 0.699593792047251, -0.234545309910987,
0.563077866073106, 1.41270865461162, 0.351700547445558, -0.417798927400163,
1.04741309069348)), row.names = c(NA, -50L), class = c("tbl_df",
"tbl", "data.frame")))
我正在尝试使用 geom_rec 来遮蔽带有小平面的线图区域...但是，我得到的最好的是这个 。
这就是我得到那张图片的方式
library(tidyverse)
SKOV_2<-SKOV%>%
pivot_longer(3:4, values_to="conc", names_to="s_elements")%>%mutate(s_elements=as.factor(s_elements))
SKOV_2
ggplot(data=SKOV_2, aes(x=sample_id,y=conc ,colour = factor(s_elements))) + #
geom_rect(aes(xmin = sample_id, xmax = dplyr::lead(sample_id), ymin = min(conc), ymax = Inf, fill = cluster),
alpha = 0.5) +
geom_line(show.legend = FALSE, lwd = 0.8) +
facet_wrap(~ s_elements, scales = "free") +
coord_flip() +
scale_x_reverse()
我希望做的每个方面都类似于这张图片。
我已经查看了 this question and this ...，但它们与我想要做的事情有些相去甚远。任何指导将不胜感激。实际的数据集很大，无法作为 dput 共享，但如果需要，我可以提供。主要思想是使用“clust”列进行阴影。
有几点需要注意。如果您要使用 lead，您需要确保样本 ID 在其组内排序。其次，要使背景矩形起作用，您可能需要删除轮廓。第三，您可能需要将 alpha 降低；
library(tidyverse)
SKOV %>%
pivot_longer(3:4, values_to="conc", names_to="s_elements") %>%
mutate(s_elements = as.factor(s_elements)) %>%
group_by(s_elements) %>%
arrange(sample_id, by_group = TRUE) %>%
mutate(sample_id_lead = dplyr::lead(sample_id)) %>%
ggplot(aes(x =sample_id, y = conc ,colour = factor(s_elements))) +
geom_rect(aes(xmin = sample_id, xmax = sample_id_lead,
ymin = min(conc), ymax = Inf, fill = cluster),
alpha = 0.1, color = NA) +
scale_fill_manual(values = c("red", "blue", "green")) +
geom_line(show.legend = FALSE, lwd = 0.8) +
facet_wrap(~ s_elements, scales = "free") +
coord_flip() +
scale_x_reverse()
这是我的数据输入样本...
SKOV<-as.data.frame(structure(list(sample_id = c(147, 167, 18, 131, 17, 222, 270,
19, 173, 56, 265, 151, 250, 273, 17, 61, 324, 279, 161, 144,
312, 97, 88, 167, 112, 146, 30, 17, 52, 99, 188, 135, 184, 277,
93, 305, 37, 264, 253, 74, 299, 176, 93, 24, 162, 7, 209, 259,
85, 231), cluster = structure(c(3L, 3L, 1L, 3L, 3L, 2L, 1L, 1L,
3L, 3L, 1L, 3L, 2L, 1L, 3L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 3L, 3L,
1L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 1L, 2L, 3L,
2L, 3L, 3L, 1L, 2L, 1L, 1L, 2L, 3L, 3L), .Label = c("1", "2",
"3"), class = "factor"), PC1 = c(3.77541554972203, 1.45135092275582,
-2.08151488022046, 1.67392278661776, -0.868667705111095, 0.381346152049824,
-4.51990813842073, -4.33106175029972, 4.09484115180022, 1.46878082673118,
-3.91009236070008, 2.29855909444833, 1.28996975316864, -4.33482864589876,
-0.868667705111095, 1.71386877911134, 0.542339094466148, -5.42446660572953,
2.07724803679489, 3.1482373274481, 0.319519012725789, 1.35932545954681,
0.515976962035749, 1.45135092275582, -2.2956688398855, 3.12130887968216,
0.957815542171644, -0.868667705111095, 2.44038404221629, -3.73532585246298,
3.60298231127924, 1.76697731520925, 0.107335593841895, -4.24926391802412,
2.37822851276455, 0.863188301906724, 0.633358640568618, -4.49426173195687,
0.657329751847109, 1.07849849754947, 0.949226371513706, 2.20251690907612,
2.37822851276455, -5.27188001219925, 0.883552693512362, -3.65471472570778,
-4.17396534181865, 0.740413932072948, 2.35215180710275, 2.95723653631208
), PC2 = c(2.9821460311606, 1.84547428893804, -0.110154674255345,
0.94494220934088, 1.77529975625982, -1.75627062956882, 0.455969541574471,
1.01232365128454, 5.22885669105582, 0.614756183817838, 0.53924434139561,
0.824937608066585, -2.58029550589492, 0.599258675742853, 1.77529975625982,
-1.51359310918018, -2.94834599434092, 1.06948580574065, -1.53223407015238,
2.15612285827502, -3.11118425549662, 1.35847952064022, 1.52107198979851,
1.84547428893804, 0.600591536702616, 0.873482585063045, -2.66401802898751,
1.77529975625982, 1.72968551305468, 1.57607478071461, 4.6737233499792,
0.407336067372946, 2.67228082439395, 0.116730668123481, 3.76755528568674,
-3.38651333356804, -1.44641388322638, 0.154407055925795, -2.43105409429423,
0.644629065490083, -1.74893772151854, 2.74702223077597, 3.76755528568674,
1.36129565011855, -0.771286443151456, 1.26640510339257, -0.24592726728564,
-2.12450533889367, 1.59309393261233, 0.536480763301413), PC3 = c(-1.54368524007348,
2.43402556007619, -0.0450967184646642, 0.0410426039829931, -0.848801725782723,
0.38804666076165, -1.53725160414506, 0.00144436967650244, -0.219129975234483,
-0.549564648310951, 0.404446222294109, -0.2024943392322, -0.151863586122569,
0.595219147562226, -0.848801725782723, -0.546704676736899, 0.603107536227402,
-3.84773521184464, -1.12569943452765, -0.784183732377998, 0.954584210395496,
1.03620198922004, 2.88552053445592, 2.43402556007619, -6.37932899691359,
-0.440575910698521, 0.577066781163853, -0.848801725782723, 0.0762964729013986,
-0.197383547463127, -0.122216505063875, -0.152129782123152, 5.9139111613348,
0.419059172110045, 0.538964366314296, 0.288559910935336, 0.573620970938223,
-1.43282961184911, 0.273788799449918, 1.17945429175878, -0.939838485321215,
0.769679221867679, 0.538964366314296, 0.154922166837421, 0.640999296534156,
-0.213535320971047, 0.487374487637418, 0.530768949185841, 0.682768810474348,
-2.65577976399495), PC4 = c(0.513667495685775, -1.61889899346387,
-0.00199956789317688, 0.0852133725033396, 0.200110415721805,
-0.54476643944572, -0.665890345796119, 0.692033116930379, 0.68095691640909,
0.0715306855842227, 1.56182228831717, 0.836306992758303, -0.213466309266933,
1.25649126097911, 0.200110415721805, 0.329995819652711, -0.673049170161758,
-7.16504510762261, -0.00823529898926304, 0.636191153685101, -0.112239279821607,
-0.668345916270364, -2.22187934484341, -1.61889899346387, -5.79832117606908,
0.679388729196689, -0.565063309411897, 0.200110415721805, 0.277981223741711,
0.240500293392891, 0.424228959808187, 0.188958927083737, -4.88485900638901,
1.39045390873317, 0.044539479926183, -0.39937436237614, -0.127318966653892,
-3.92777799888502, 0.106028414733035, -1.35733137106087, 0.604053687622463,
-0.283098175950597, 0.044539479926183, 0.699593792047251, -0.234545309910987,
0.563077866073106, 1.41270865461162, 0.351700547445558, -0.417798927400163,
1.04741309069348)), row.names = c(NA, -50L), class = c("tbl_df",
"tbl", "data.frame")))
我正在尝试使用 geom_rec 来遮蔽带有小平面的线图区域...但是，我得到的最好的是这个
这就是我得到那张图片的方式
library(tidyverse)
SKOV_2<-SKOV%>%
pivot_longer(3:4, values_to="conc", names_to="s_elements")%>%mutate(s_elements=as.factor(s_elements))
SKOV_2
ggplot(data=SKOV_2, aes(x=sample_id,y=conc ,colour = factor(s_elements))) + #
geom_rect(aes(xmin = sample_id, xmax = dplyr::lead(sample_id), ymin = min(conc), ymax = Inf, fill = cluster),
alpha = 0.5) +
geom_line(show.legend = FALSE, lwd = 0.8) +
facet_wrap(~ s_elements, scales = "free") +
coord_flip() +
scale_x_reverse()
我希望做的每个方面都类似于这张图片
我已经查看了 this question and this
有几点需要注意。如果您要使用 lead，您需要确保样本 ID 在其组内排序。其次，要使背景矩形起作用，您可能需要删除轮廓。第三，您可能需要将 alpha 降低；
library(tidyverse)
SKOV %>%
pivot_longer(3:4, values_to="conc", names_to="s_elements") %>%
mutate(s_elements = as.factor(s_elements)) %>%
group_by(s_elements) %>%
arrange(sample_id, by_group = TRUE) %>%
mutate(sample_id_lead = dplyr::lead(sample_id)) %>%
ggplot(aes(x =sample_id, y = conc ,colour = factor(s_elements))) +
geom_rect(aes(xmin = sample_id, xmax = sample_id_lead,
ymin = min(conc), ymax = Inf, fill = cluster),
alpha = 0.1, color = NA) +
scale_fill_manual(values = c("red", "blue", "green")) +
geom_line(show.legend = FALSE, lwd = 0.8) +
facet_wrap(~ s_elements, scales = "free") +
coord_flip() +
scale_x_reverse()